Changes in the anatomical structure of the human brain occur as a normal part of the development and aging process, and in response to many disease and injury processes. In addition, recent success in functional neuroimaging has resulted in significant interest in the relationship between structural morphology and function. The ability to detect and monitor structural changes directly influences our ability to diagnose, treat and understand changes which associated with deleterious conditions. Magnetic Resonance Imaging (MRI) has been shown to be the most effective high-resolution, non-invasive means of assessing the structure of the intact human brain. Functional measures, such as metabolism and cerebral vascular hemodynamics, are important adjuncts to the diagnostic process. The goal of this grant is the quantitation of significant features in MRI images of the brain and their temporal evolution in a robust and reproducible fashion. To achieve this objective, we propose to improve quantitation of morphological features of brain structures through (i) improved automation of the segmentation and classification task, and (ii) improved representation and quantitation of the shapes of such structures. Several unique aspects of this proposal distinguishes it from past work performed in this area. First, a unified framework for segmentation and classification, as well as quantitation and representation of morphology in three dimensions is suggested. Second, this unified framework will incorporate the multi-spectral nature of MRI data, and will yield characterizations of uncertainties associated with the estimation process - thus permitting the rational analysis of the sensitivity of the techniques developed. Third, the approach set forth a systematic and incremental means of evaluating pixel classification performance - from phantoms to research quality to clinical quality data. Fourth, the approach expands upon traditional "static" image analysis by incorporating and extracting time evolution statistics for data analysis and anomaly screening.